Data Scientist

About this role

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Skills you'll use

What you'll do

• End-to-End Data Science Ownership: Own extraction and enrichment problems from discovery through production and continuous improvement. Define the problem, select data and methods, establish success criteria, evaluate results, and monitor outcomes
• Problem Formulation & Data Strategy: Translate business requirements into measurable data science problems. Explore structured and unstructured data, identify quality and source-variability issues, and define training, validation, test, and labeling requirements with domain partners
• Model Development & Experimentation: Design and optimize NLP, machine learning, and LLM solutions for document understanding and information extraction
• Extraction Solution Development: Build extraction workflows using document parsing, preprocessing, chunking, feature engineering, embeddings, RAG, prompt engineering, fine-tuning, and agentic approaches
• Evaluation & Error Analysis: Create representative evaluation datasets and metrics such as precision, recall, F1, field-level accuracy, coverage, confidence, and business impact. Use error analysis to guide model, prompt, data, and workflow improvements
• Productionization & Model Ownership: Develop robust, testable model components and partner with ML Engineers to integrate solutions into production. Monitor quality, investigate regressions or drift, and prioritize improvements based on customer and business impact
• Technical Trade-offs & Quality: Evaluate accuracy, coverage, latency, scalability, robustness, and cost. Recommend approaches using empirical evidence, write maintainable code, and document datasets, assumptions, experiments, limitations, and results
• Collaboration & Innovation: Partner with Product, Data Collection, Engineering, Platform, and domain teams. Evaluate advances in NLP, generative AI, LLMs, and information extraction, and apply methods that deliver measurable value

Who can apply

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Economics, Engineering, or a related quantitative field
• 2+ years of experience in applied data science, machine learning, NLP, or information extraction
• Demonstrated experience taking a data science or machine learning solution from problem definition and experimentation through production launch and ongoing improvement
• Experience analyzing large, complex structured and unstructured datasets, including exploration, preprocessing, feature engineering, sampling, labeling, and dataset construction
• Hands-on experience developing document intelligence or information-extraction solutions using techniques such as transformers, embeddings, RAG, LLMs, prompt engineering, fine-tuning, or agentic workflows
• Strong understanding of experimental design, statistical reasoning, model evaluation, error analysis, and metrics such as precision, recall, F1, field-level accuracy, confidence, and coverage
• Proficiency in Python and SQL, with experience using pandas, NumPy, scikit-learn, and PyTorch or TensorFlow
• Experience with Hugging Face, LangChain, or comparable NLP and LLM frameworks; ability to write maintainable, testable model and data-processing code
• Familiarity with cloud ML environments, version control, automated testing, model monitoring, containers, or data orchestration tools is beneficial
• Strong communication and collaboration skills, including the ability to explain model behavior, limitations, trade-offs, and recommendations; experience with financial data, document intelligence, or large-scale data collection is a plus

About Morningstar

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